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The Lancet Digital Health
Number of Followers: 2  

  This is an Open Access Journal Open Access journal
ISSN (Online) 2589-7500
Published by Elsevier Homepage  [3206 journals]
  • Early epidemiological analysis of the coronavirus disease 2019 outbreak
           based on crowdsourced data: a population-level observational study

    • Abstract: Publication date: Available online 20 February 2020Source: The Lancet Digital HealthAuthor(s): Kaiyuan Sun, Jenny Chen, Cécile ViboudSummaryBackgroundAs the outbreak of coronavirus disease 2019 (COVID-19) progresses, epidemiological data are needed to guide situational awareness and intervention strategies. Here we describe efforts to compile and disseminate epidemiological information on COVID-19 from news media and social networks.MethodsIn this population-level observational study, we searched DXY.cn, a health-care-oriented social network that is currently streaming news reports on COVID-19 from local and national Chinese health agencies. We compiled a list of individual patients with COVID-19 and daily province-level case counts between Jan 13 and Jan 31, 2020, in China. We also compiled a list of internationally exported cases of COVID-19 from global news media sources (Kyodo News, The Straits Times, and CNN), national governments, and health authorities. We assessed trends in the epidemiology of COVID-19 and studied the outbreak progression across China, assessing delays between symptom onset, seeking care at a hospital or clinic, and reporting, before and after Jan 18, 2020, as awareness of the outbreak increased. All data were made publicly available in real time.FindingsWe collected data for 507 patients with COVID-19 reported between Jan 13 and Jan 31, 2020, including 364 from mainland China and 143 from outside of China. 281 (55%) patients were male and the median age was 46 years (IQR 35–60). Few patients (13 [3%]) were younger than 15 years and the age profile of Chinese patients adjusted for baseline demographics confirmed a deficit of infections among children. Across the analysed period, delays between symptom onset and seeking care at a hospital or clinic were longer in Hubei province than in other provinces in mainland China and internationally. In mainland China, these delays decreased from 5 days before Jan 18, 2020, to 2 days thereafter until Jan 31, 2020 (p=0·0009). Although our sample captures only 507 (5·2%) of 9826 patients with COVID-19 reported by official sources during the analysed period, our data align with an official report published by Chinese authorities on Jan 28, 2020.InterpretationNews reports and social media can help reconstruct the progression of an outbreak and provide detailed patient-level data in the context of a health emergency. The availability of a central physician-oriented social network facilitated the compilation of publicly available COVID-19 data in China. As the outbreak progresses, social media and news reports will probably capture a diminishing fraction of COVID-19 cases globally due to reporting fatigue and overwhelmed health-care systems. In the early stages of an outbreak, availability of public datasets is important to encourage analytical efforts by independent teams and provide robust evidence to guide interventions.FundingFogarty International Center, US National Institutes of Health.
       
  • COVID-19 and artificial intelligence: protecting health-care workers and
           curbing the spread

    • Abstract: Publication date: Available online 20 February 2020Source: The Lancet Digital HealthAuthor(s): Becky McCall
       
  • Crowdsourcing data to mitigate epidemics

    • Abstract: Publication date: Available online 20 February 2020Source: The Lancet Digital HealthAuthor(s): Gabriel M Leung, Kathy Leung
       
  • Further evaluation is required for smartphone-aided diagnosis of skin
           cancer

    • Abstract: Publication date: Available online 20 February 2020Source: The Lancet Digital HealthAuthor(s): Fiona M Walter, Jon D Emery
       
  • The future of radiomics in lung cancer

    • Abstract: Publication date: Available online 13 February 2020Source: The Lancet Digital HealthAuthor(s): Rajat Thawani, Syed Atif Mustafa
       
  • CT derived radiomic score for predicting the added benefit of adjuvant
           chemotherapy following surgery in stage I, II resectable non-small cell
           lung cancer: a retrospective multicohort study for outcome prediction

    • Abstract: Publication date: Available online 13 February 2020Source: The Lancet Digital HealthAuthor(s): Pranjal Vaidya, Kaustav Bera, Amit Gupta, Xiangxue Wang, Germán Corredor, Pingfu Fu, Niha Beig, Prateek Prasanna, Pradnya D Patil, Priya D Velu, Prabhakar Rajiah, Robert Gilkeson, Michael D Feldman, Humberto Choi, Vamsidhar Velcheti, Anant MadabhushiSummaryBackgroundUse of adjuvant chemotherapy in patients with early-stage lung cancer is controversial because no definite biomarker exists to identify patients who would receive added benefit from it. We aimed to develop and validate a quantitative radiomic risk score (QuRiS) and associated nomogram (QuRNom) for early-stage non-small cell lung cancer (NSCLC) that is prognostic of disease-free survival and predictive of the added benefit of adjuvant chemotherapy following surgery.MethodsWe did a retrospective multicohort study of individuals with early-stage NSCLC (stage I and II) who either received surgery alone or surgery plus adjuvant chemotherapy. We selected patients for whom we had available pre-treatment diagnostic CT scans and corresponding survival information. We used radiomic texture features derived from within and outside the primary lung nodule on chest CT scans of patients from the Cleveland Clinic Foundation (Cleveland, OH, USA; cohort D1) to develop QuRiS. A least absolute shrinkage and selection operator-Cox regularisation model was used for data dimension reduction, feature selection, and QuRiS construction. QuRiS was independently validated on a cohort of patients from the University of Pennsylvania (Philadephia, PA, USA; cohort D2) and a cohort of patients whose CT scans were derived from The Cancer Imaging Archive (cohort D3). QuRNom was constructed by integrating QuRiS with tumour and node descriptors (according to the tumour, node, metastasis staging system) and lymphovascular invasion. The primary endpoint of the study was the assessment of the performance of QuRiS and QuRNom in predicting disease-free survival. The added benefit of adjuvant chemotherapy estimated using QuRiS and QuRNom was validated by comparing patients who received adjuvant chemotherapy versus patients who underwent surgery alone in cohorts D1–D3.FindingsWe included: 329 patients in cohort D1 (73 [22%] had surgery plus adjuvant chemotherapy and 256 (78%) had surgery alone); 114 patients in cohort D2 (33 [29%] had surgery plus adjuvant chemotherapy and 81 (71%) had surgery alone); and 82 patients in cohort D3 (24 [29%] had surgery plus adjuvant chemotherapy and 58 (71%) had surgery alone). QuRiS comprised three intratumoral and 10 peritumoral CT-radiomic features and was found to be significantly associated with disease-free survival (ie, prognostic validation of QuRiS; hazard ratio for predicted high-risk vs predicted low-risk groups 1·56, 95% CI 1·08–2·23, p=0·016 for cohort D1; 2·66, 1·24–5·68, p=0·011 for cohort D2; and 2·67, 1·39–5·11, p=0·0029 for cohort D3). To validate the predictive performance of QuRiS, patients were partitioned into three risk groups (high, intermediate, and low risk) on the basis of their corresponding QuRiS. Patients in the high-risk group were observed to have significantly longer survival with adjuvant chemotherapy than patients who underwent surgery alone (0·27, 0·08–0·95, p=0·042, for cohort D1; 0·08, 0·01–0·42, p=0·0029, for cohorts D2 and D3 combined). As concerns QuRNom, the nomogram-estimated survival benefit was predictive of the actual efficacy of adjuvant chemotherapy (0·25, 0·12–0·55, p
       
  • Evaluating AI in breast cancer screening: a complex task

    • Abstract: Publication date: Available online 6 February 2020Source: The Lancet Digital HealthAuthor(s): Magnus Dustler
       
  • Changes in cancer detection and false-positive recall in mammography using
           artificial intelligence: a retrospective, multireader study

    • Abstract: Publication date: Available online 6 February 2020Source: The Lancet Digital HealthAuthor(s): Hyo-Eun Kim, Hak Hee Kim, Boo-Kyung Han, Ki Hwan Kim, Kyunghwa Han, Hyeonseob Nam, Eun Hye Lee, Eun-Kyung KimSummaryBackgroundMammography is the current standard for breast cancer screening. This study aimed to develop an artificial intelligence (AI) algorithm for diagnosis of breast cancer in mammography, and explore whether it could benefit radiologists by improving accuracy of diagnosis.MethodsIn this retrospective study, an AI algorithm was developed and validated with 170 230 mammography examinations collected from five institutions in South Korea, the USA, and the UK, including 36 468 cancer positive confirmed by biopsy, 59 544 benign confirmed by biopsy (8827 mammograms) or follow-up imaging (50 717 mammograms), and 74 218 normal. For the multicentre, observer-blinded, reader study, 320 mammograms (160 cancer positive, 64 benign, 96 normal) were independently obtained from two institutions. 14 radiologists participated as readers and assessed each mammogram in terms of likelihood of malignancy (LOM), location of malignancy, and necessity to recall the patient, first without and then with assistance of the AI algorithm. The performance of AI and radiologists was evaluated in terms of LOM-based area under the receiver operating characteristic curve (AUROC) and recall-based sensitivity and specificity.FindingsThe AI standalone performance was AUROC 0·959 (95% CI 0·952–0·966) overall, and 0·970 (0·963–0·978) in the South Korea dataset, 0·953 (0·938–0·968) in the USA dataset, and 0·938 (0·918–0·958) in the UK dataset. In the reader study, the performance level of AI was 0·940 (0·915–0·965), significantly higher than that of the radiologists without AI assistance (0·810, 95% CI 0·770–0·850; p
       
  • The Tanzanian digital health agenda

    • Abstract: Publication date: February 2020Source: The Lancet Digital Health, Volume 2, Issue 2Author(s): Geoff Watts
       
  • Prognostic models in first-episode psychosis – Authors' reply

    • Abstract: Publication date: February 2020Source: The Lancet Digital Health, Volume 2, Issue 2Author(s): Samuel P Leighton, Max Birchwood, Pavan K Mallikarjun
       
  • Prognostic models in first-episode psychosis

    • Abstract: Publication date: February 2020Source: The Lancet Digital Health, Volume 2, Issue 2Author(s): Daniel Whiting, Seena Fazel
       
  • Precision global health for real-time action

    • Abstract: Publication date: February 2020Source: The Lancet Digital Health, Volume 2, Issue 2Author(s): Antoine Flahault, Jürg Utzinger, Isabella Eckerle, Danny J Sheath, Rafael Ruiz de Castañeda, Isabelle Bolon, Nefti-Eboni Bempong, Fred Andayi
       
  • Leaving cancer diagnosis to the computers

    • Abstract: Publication date: February 2020Source: The Lancet Digital Health, Volume 2, Issue 2Author(s): The Lancet Digital Health
       
  • Fitbit-informed influenza forecasts

    • Abstract: Publication date: Available online 16 January 2020Source: The Lancet Digital HealthAuthor(s): Cecile Viboud, Mauricio Santillana
       
  • Closed-loop insulin delivery: understanding when and how it is effective

    • Abstract: Publication date: Available online 3 January 2020Source: The Lancet Digital HealthAuthor(s): Pierre-Yves Benhamou, Yves Reznik
       
  • Combined prevention for substance use, depression, and anxiety in
           adolescence: a cluster-randomised controlled trial of a digital online
           intervention

    • Abstract: Publication date: Available online 3 January 2020Source: The Lancet Digital HealthAuthor(s): Maree Teesson, Nicola C Newton, Tim Slade, Cath Chapman, Louise Birrell, Louise Mewton, Marius Mather, Leanne Hides, Nyanda McBride, Steve Allsop, Gavin AndrewsSummaryBackgroundSubstance use, depression, and anxiety in adolescence are major public health problems requiring new scalable prevention strategies. We aimed to assess the effectiveness of a combined online universal (ie, delivered to all pupils) school-based preventive intervention targeting substance use, depression, and anxiety in adolescence.MethodsWe did a multicentre, cluster-randomised controlled trial in secondary schools in Australia, with pupils in year 8 or 9 (aged 13–14 years). Participating schools were randomly assigned (1:1:1:1) to one of four intervention conditions: (1) Climate Schools–Substance Use, focusing on substance use only; (2) Climate Schools–Mental Health, focusing on depression and anxiety only; (3) Climate Schools–Combined, focusing on the prevention of substance use, depression, and anxiety; or (4) active control. The interventions were delivered in school classrooms in an online delivery format and used a mixture of peer cartoon storyboards and classroom activities that were focused on alcohol, cannabis, anxiety, and depression. The interventions were delivered for 2 years and primary outcomes were knowledge related to alcohol, cannabis, and mental health; alcohol use, including heavy episodic drinking; and depression and anxiety symptoms at 12, 24, and 30 months after baseline. This trial is registered with the Australian New Zealand Clinical Trials Registry (ACTRN12613000723785) and an extended follow-up is underway.FindingsBetween Sept 1, 2013, and Feb 28, 2014, we recruited 88 schools (12 391 pupils), of whom 71 schools and 6386 (51·5%) pupils were analysed (17 schools dropped out and 1308 pupils declined to participate). We allocated 18 schools (1739 [27·25%] pupils; 1690 [97·2%] completed at least one follow-up) to the substance use condition, 18 schools (1594 [25·0%] pupils; 1560 [97·9%] completed at least one follow-up) to the mental health condition, 16 schools (1497 [23·4%] pupils; 1443 [96·4%] completed at least one follow-up) to the combined condition, and 19 schools (1556 [23·4%] pupils; 1513 [97·2%] completed at least one follow-up) to the control condition. Compared with controls, the combined intervention group had increased knowledge related to alcohol and cannabis at 12, 24, and 30 months (standardised mean difference [SMD] for alcohol 0·26 [95% CI 0·14 to 0·39] and for cannabis 0·17 [0·06 to 0·28] at 30 months), increased knowledge related to mental health at 24 months (0·17 [0·08 to 0·27]), reduced growth in their odds of drinking and heavy episodic drinking at 12, 24, and 30 months (odds ratio for drinking 0·25 [95% CI 0·12 to 0·51], and for heavy episodic drinking 0·15 [0·04 to 0·58] at 30 months), and reduced increases in anxiety symptoms at 12 and 30 months (SMD −0·12 [95% CI −0·22 to −0·01] at 30 months). We found no difference in symptoms or probable diagnosis of depression. The combined intervention group also showed improvement in alcohol use outcomes compared with the substance use and mental health interventions and improvements in anxiety outcomes when compared with the mental health intervention only.InterpretationCombined online prevention of substance use, depression, and anxiety led to increased knowledge of alcohol, cannabis, and mental health, reduced increase in the odds of any drinking and heavy episodic drinking, and reduced symptoms of anxiety over a 30-month period. These findings provide the first evidence of the effectiveness of an online universal school-based preventive intervention targeting substance use, depression, and anxiety in adolescence.FundingAustralian National Health and Medical Research Council.
       
  • An online school-based prevention programme targeting substance use,
           depression, and anxiety in adolescence: improving impact and accessibility
           

    • Abstract: Publication date: Available online 3 January 2020Source: The Lancet Digital HealthAuthor(s): Catia Magalhães, Bruno Carraça
       
  • Ensuring that the NHS realises fair financial value from its data

    • Abstract: Publication date: January 2020Source: The Lancet Digital Health, Volume 2, Issue 1Author(s): Gianluca Fontana, Saira Ghafur, Lydia Torne, Jonathan Goodman, Ara Darzi
       
  • Artificial intelligence, the internet of things, and virtual clinics:
           ophthalmology at the digital translation forefront

    • Abstract: Publication date: January 2020Source: The Lancet Digital Health, Volume 2, Issue 1Author(s): Daniel S W Ting, Haotian Lin, Paisan Ruamviboonsuk, Tien Yin Wong, Dawn A Sim
       
  • Finding Barrett's oesophagus: is there a machine learning approach in our
           future'

    • Abstract: Publication date: January 2020Source: The Lancet Digital Health, Volume 2, Issue 1Author(s): Kenneth K Wang, Cadman Leggett
       
  • Remote management of patients with heart failure—how long should it
           go on'

    • Abstract: Publication date: January 2020Source: The Lancet Digital Health, Volume 2, Issue 1Author(s): Niraj Varma
       
  • New beginnings

    • Abstract: Publication date: January 2020Source: The Lancet Digital Health, Volume 2, Issue 1Author(s): The Lancet Digital Health
       
  • Development and validation of a risk prediction model to diagnose
           Barrett's oesophagus (MARK-BE): a case-control machine learning approach

    • Abstract: Publication date: January 2020Source: The Lancet Digital Health, Volume 2, Issue 1Author(s): Avi Rosenfeld, David G Graham, Sarah Jevons, Jose Ariza, Daryl Hagan, Ash Wilson, Samuel J Lovat, Sarmed S Sami, Omer F Ahmad, Marco Novelli, Manuel Rodriguez Justo, Alison Winstanley, Eliyahu M Heifetz, Mordehy Ben-Zecharia, Uria Noiman, Rebecca C Fitzgerald, Peter Sasieni, Laurence B Lovat, Karen Coker, Wanfeng ZhaoSummaryBackgroundScreening for Barrett's oesophagus relies on endoscopy, which is invasive and few who undergo the procedure are found to have the condition. We aimed to use machine learning techniques to develop and externally validate a simple risk prediction panel to screen individuals for Barrett's oesophagus.MethodsIn this prospective study, machine learning risk prediction in Barrett's oesophagus (MARK-BE), we used data from two case-control studies, BEST2 and BOOST, to compile training and validation datasets. From the BEST2 study, we analysed questionnaires from 1299 patients, of whom 880 (67·7%) had Barrett's oesophagus, including 40 with invasive oesophageal adenocarcinoma, and 419 (32·3%) were controls. We randomly split (6:4) the cohort using a computer algorithm into a training dataset of 776 patients and a testing dataset of 523 patients. We compiled an external validation cohort from the BOOST study, which included 398 patients, comprising 198 patients with Barrett's oesophagus (23 with oesophageal adenocarcinoma) and 200 controls. We identified independently important diagnostic features of Barrett's oesophagus using the machine learning techniques information gain and correlation-based feature selection. We assessed multiple classification tools to create a multivariable risk prediction model. Internal validation of the model using the BEST2 testing dataset was followed by external validation using the BOOST external validation dataset. From these data we created a prediction panel to identify at-risk individuals.FindingsThe BEST2 study included 40 diagnostic features. Of these, 19 added information gain but after correlation-based feature selection only eight showed independent diagnostic value including age, sex, cigarette smoking, waist circumference, frequency of stomach pain, duration of heartburn and acidic taste, and taking antireflux medication, of which all were associated with increased risk of Barrett's oesophagus, except frequency of stomach pain, with was inversely associated in a case-control population. Logistic regression offered the highest prediction quality with an area under the receiver-operator curve (AUC) of 0·87 (95% CI 0·84–0·90; sensitivity set at 90%; specificity of 68%). In the testing dataset, AUC was 0·86 (0·83–0·89; sensitivity set at 90%; specificity of 65%). In the external validation dataset, the AUC was 0·81 (0·74–0·84; sensitivity set at 90%; specificity of 58%).InterpretationOur diagnostic model offers valid predictions of diagnosis of Barrett's oesophagus in patients with symptomatic gastro-oesophageal reflux disease, assisting in identifying who should go forward to invasive confirmatory testing. Our predictive panel suggests that overweight men who have been taking antireflux medication for a long time might merit particular consideration for further testing. Our risk prediction panel is quick and simple to administer but will need further calibration and validation in a prospective study in primary care.FundingCharles Wolfson Charitable Trust and Guts UK.
       
  • Genetic apps: raising more questions than they answer'

    • Abstract: Publication date: January 2020Source: The Lancet Digital Health, Volume 2, Issue 1Author(s): Talha Burki
       
  • Value and limitations of real-world data to understand paediatric
           adherence to positive airway pressure therapy

    • Abstract: Publication date: Available online 23 December 2019Source: The Lancet Digital HealthAuthor(s): Robinder G Khemani
       
  • Digital self-help interventions for suicidal ideation and behaviour

    • Abstract: Publication date: Available online 29 November 2019Source: The Lancet Digital HealthAuthor(s): Eirini Karyotaki, Wouter van Ballegooijen
       
  • Correction to Lancet Digital Health 2019; published online Oct 10.
           https://doi.org/10.1016/S2589-7500(19)30151-7

    • Abstract: Publication date: Available online 16 October 2019Source: The Lancet Digital HealthAuthor(s):
       
 
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